Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review

Autores
Soto, Salvador; Pollo Cattaneo, Ma. Florencia; Yepes Calderon, Fernando
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Prostate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure, to specify materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms intending to find the reasons precluding the solutions from being masively adopted in the standats of care
Fil: Pollo Cattaneo, Ma. Florencia; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina
Fil: Yepes Calderon, Fernando; GYM Group SA - Departamento I+R; Colombia
Fil: Soto, Salvador; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina
Peer Reviewed
Materia
Artificial intelligence
Prostate cancer
diagnosis
Automatic pathology diagnosis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-07-12T17:05:16Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/11123
Acceder
id RIAUTN_93b4b1a354d0253fc502add876cda40d
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/11123
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature reviewSoto, SalvadorPollo Cattaneo, Ma. FlorenciaYepes Calderon, FernandoArtificial intelligenceProstate cancerdiagnosisAutomatic pathology diagnosisProstate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure, to specify materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms intending to find the reasons precluding the solutions from being masively adopted in the standats of careFil: Pollo Cattaneo, Ma. Florencia; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Yepes Calderon, Fernando; GYM Group SA - Departamento I+R; ColombiaFil: Soto, Salvador; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaPeer Reviewed2024-07-12T17:05:16Z2024-07-12T17:05:16Z2023-10-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfSoto, S.; Pollo-Cattaneo, M. F. & Yepes-Calderon, F. (2023). “Automated Diagnosis of Prostate Cancer Using Artificial Intelligence. A Systematic Literature Review”. En “6th International Conference on Applied Informatics” (ICAI 2023). CCIS Series Volume 1874. Pages 77-92. Springer International Publishing. 26 al 28 Octubre 2023 - ISBN-e: 978-3-031-46813-1. ISSN: 1865-0929. ISSN-e: 1865-0937 - DOI: https://doi.org/10.1007/978-3-031-46813-1978-3-031-46813-11865-0929http://hdl.handle.net/20.500.12272/1112310.1007/978-3-031-46813-1enginfo:eu-repo/semantics/openAccess2024-07-12T17:05:16Z-reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:47:19Zoai:ria.utn.edu.ar:20.500.12272/11123instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:47:21.347Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
title Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
spellingShingle Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
Soto, Salvador
Artificial intelligence
Prostate cancer
diagnosis
Automatic pathology diagnosis
title_short Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
title_full Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
title_fullStr Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
title_full_unstemmed Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
title_sort Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review
dc.creator.none.fl_str_mv Soto, Salvador
Pollo Cattaneo, Ma. Florencia
Yepes Calderon, Fernando
author Soto, Salvador
author_facet Soto, Salvador
Pollo Cattaneo, Ma. Florencia
Yepes Calderon, Fernando
author_role author
author2 Pollo Cattaneo, Ma. Florencia
Yepes Calderon, Fernando
author2_role author
author
dc.subject.none.fl_str_mv Artificial intelligence
Prostate cancer
diagnosis
Automatic pathology diagnosis
topic Artificial intelligence
Prostate cancer
diagnosis
Automatic pathology diagnosis
dc.description.none.fl_txt_mv Prostate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure, to specify materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms intending to find the reasons precluding the solutions from being masively adopted in the standats of care
Fil: Pollo Cattaneo, Ma. Florencia; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina
Fil: Yepes Calderon, Fernando; GYM Group SA - Departamento I+R; Colombia
Fil: Soto, Salvador; Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina
Peer Reviewed
description Prostate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure, to specify materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms intending to find the reasons precluding the solutions from being masively adopted in the standats of care
publishDate 2023
dc.date.none.fl_str_mv 2023-10-28
2024-07-12T17:05:16Z
2024-07-12T17:05:16Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Soto, S.; Pollo-Cattaneo, M. F. & Yepes-Calderon, F. (2023). “Automated Diagnosis of Prostate Cancer Using Artificial Intelligence. A Systematic Literature Review”. En “6th International Conference on Applied Informatics” (ICAI 2023). CCIS Series Volume 1874. Pages 77-92. Springer International Publishing. 26 al 28 Octubre 2023 - ISBN-e: 978-3-031-46813-1. ISSN: 1865-0929. ISSN-e: 1865-0937 - DOI: https://doi.org/10.1007/978-3-031-46813-1
978-3-031-46813-1
1865-0929
http://hdl.handle.net/20.500.12272/11123
10.1007/978-3-031-46813-1
identifier_str_mv Soto, S.; Pollo-Cattaneo, M. F. & Yepes-Calderon, F. (2023). “Automated Diagnosis of Prostate Cancer Using Artificial Intelligence. A Systematic Literature Review”. En “6th International Conference on Applied Informatics” (ICAI 2023). CCIS Series Volume 1874. Pages 77-92. Springer International Publishing. 26 al 28 Octubre 2023 - ISBN-e: 978-3-031-46813-1. ISSN: 1865-0929. ISSN-e: 1865-0937 - DOI: https://doi.org/10.1007/978-3-031-46813-1
978-3-031-46813-1
1865-0929
10.1007/978-3-031-46813-1
url http://hdl.handle.net/20.500.12272/11123
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-07-12T17:05:16Z
-
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-07-12T17:05:16Z
-
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230935915626496
score 13.265058

Publicaciones similares